Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks

Abstract

The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely scenario behind the leaves exploiting learned patterns from images with non-occluded berries. Our experiments show that the estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries. Furthermore, we show that our approach can identify which areas in the image should be changed by adding new berries without explicitly requiring information about hidden areas.

45 pages, 18 figures, 1 table

References in corpus (5)

• Conditional Generative Adversarial Nets
• Generative Adversarial Networks
• Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks
• Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields
• Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks

Cited by in corpus (4)

• Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks
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• Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds